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6 What does mobile deployment
mean? Creation Platform wars The latest handset! Technology arguments Millions of users “Will this work? Who will win?” Deployment The big winners are clear Steady, incremental improvement Commodity technology (mostly) Billions of users “What can we build with his?” As we pass 2.5bn smartphones in use, the issues that matter are changing

7 “What can we build
with this?” A new kind of scale for technology, and new kinds of computer. New ways to change other industries. Two of those industries: ecommerce & cars.

12 From important to dominant
When tech was scared of Microsoft, it was a pretty small company Source: Bloomberg, a16z GAFA Wintel All other 0 10 20 30 40 50 60 70 80 90 100 1995 2016 Top 20 US listed companies by market cap (indexed) GAFA Wintel All other

15 Today’s tech giants have
a totally different character of scale to Wintel or IBM before them. Giants in the economy, not just in tech. And (at least) four competing giants, not one.

16 So Apple is a
global top 10 retailer 500 stores, ~$25bn revenue make it a top 20 retailer. $53bn including the online store… Making Apple the 10th largest global retailer by revenue. Not bad for a marketing operation. Retail ran at 12% of revenue before Apple stopped disclosing it: direct is 25%. Source: Apple, a16z

17 0 2 4 6
8 10 12 14 Disney NBCU CBS Netflix Turner Amazon HBO Annual TV production budgets, 2015 ($bn) For Amazon, content is just a strategic lever Original content is core for Netflix, but just another way to sell Prime for Amazon. Combined, they’re 16% of US production budgets Source: IHS

18 Is Netflix a threat?
“Is the Albanian army going to take over the world?” - Jeffery Bewkes, CEO Time Warner, 2010

20 Scale of 5bn mobile
users Vast supply chain of commoditised components & platforms Scale of the winners GAFA have much greater global scale than Wintel or IBM ever had New ways to compete Custom hardware Retail & distribution Content (Cars) Building on scale Huge companies building on an ecosystem that commoditises almost everything before

22 “Is there a dog
in this picture?” After 50 years of work, computer vision systems got this right 72% of the time. A whole class of similar problems – easy for people and hard/impossible for computers. Consensus: decades more work. Then, in 2013, machine learning. Source: Imagenet

26 Since we’re analysing data,
not looking for dogs, this works for many other data sets. “Which customers are about to churn?” “Will that car let me merge?” “Is anything weird happening on the network?”

27 Alpha Go – board
games and air-con Google took the machine learning platform that won Go and applied it to data center cooling. Achieved a 15% energy saving. Building systems that find patterns in data.

29 Data New analysis on
almost any data set Interaction Images, voice, devices, new UX models Images ML can read images as easily as text? Surprises Things that don’t look like ML use cases ML use cases: how many patterns, how much data? Things that only people could do, but at a scale that people could never do

30 At the top and
bottom of the stack Training models at vast scale in the cloud… But once trained, many models could run on very small/cheap devices. A $10 widget could run on a cheap DSP with a commodity camera, looking for just one thing – people, leaks, cars…

33 Complexity and abstraction Arms
race of science, engineering and product – a new front for GAFA competition Surge in complexity Building the engineering on top of the science ‘How to build the right ML for this problem’ is becoming a new CS field Even as the science still has far to go Rush to abstraction Google, Amazon, Microsoft, IBM, etc. rushing to build ML platforms in the cloud Core, cutting-edge ML tech available to rent

34 The machine learning S-Curve
Still in the very early days of creation, with lots and lots of low-hanging fruit Creation Deployment Crazy idea Scaling Frenzy Maturity Machine Learning

38 More important: a new
generation of computing After 30 years, we shift to a new and more sophisticated kind of computer Intensely personal Always with you Imaging as primary input Touch, tilt, location, microphone App stores Ignore battery & bandwidth 1000x faster Cloud & machine learning And yet much easier to use…

39 Both more sophisticated and
easier to use New sensors, inputs and interfaces New creation: live, social, broadcast, touch, imaging New computing model…

40 Both more sophisticated and
easier to use New sensors, inputs and interfaces, machine learning New creation: live, social, broadcast, touch, imaging New computing model, and new creation models Leave the PC behind, presume mobile-only and mobile-native, not mobile-first

41 How many old assumptions
does live video break? Assume high quality camera, fast CPU/GPU for encoding and effects, touch, always on, always in your pocket. Assume unlimited bandwidth & battery. Assume 1bn high-end smartphones on earth - there can be 50 companies trying this. And turn the whole phone into the camera. Source: Houseparty, Yixia

42 Direct interaction - unbundling
the app from the phone No buttons, apps or intermediate steps – just use it. Hardware sensors as unbundled apps - deep links within apps. Apps moved to a new context. And of course all built with commodity components from the smartphone supply chain.

45 Changing friction and changing
choices Massive proliferation of UX models. All about removing friction. And choices… Removing questions and friction Do you have the printer driver? What’s your password? Where did you save that file? … What soap do you want to buy? With which app? But also choices Questions are also choices Huge strategic incentive for big platforms to be the ones to answer the question for you “Who owns the customer?”

50 Retailers as newspapers “History
doesn’t repeat, but sometimes it rhymes” Fixed cost base with falling revenue. Shift from paid to freemium, unbundling, different skills. The distribution advantage moves. New medium means different consumption – you buy different things, not just in different places.

51 Break up old bundles
and aggregators New aggregators emerge And they shape consumption in different ways Everything the internet did to media will happen to retail

54 But how do you
know what to buy? Amazon is Google for products, but we don’t have a Facebook or Buzzfeed – we don’t have suggestion and discovery. The internet lets you buy, but it doesn’t let you shop.

57 The channel is the
product Soap as a Service. New purchasing journeys mean new kinds of decisions. If you change how things are bought, you change what gets bought.

58 Train your AI on
10 years of Elle / Dwell / NYT Recipes Take a photo of your outfit / living room / kitchen counter “What would I like that I’ve never seen?” Scalable curation Scaling curation with machine learning? When can we infer taste and preference, without a purchase history or a salesperson?

65 Two paths for disrupting
cars Electric Happening right now. Removing the engine & transmission destabilizes the car industry and its suppliers. Doesn’t change how cars are used (much). Autonomy 5-10 years? Longer? Many challenges to resolve. Changes where the value is. Accelerates on-demand. Changes what cars are. Changes cities as much as cars changed cities. The growth of electric and autonomy are both, separately, changing what cars are

67 Electric unbundles cars Radical
simplification Complex, proprietary gasoline engines & transmissions disappear Replaced by simple, commodity batteries & motors 10x fewer moving parts Whole basis of competition changes New bases for competition Scale, design, distribution remain, for those who can navigate the change But value moves to software - entirely new skill set And then with autonomy, new layers of value built on top Electric makes cars much simpler and turns many elements into commodities

68 Precedent – unbundling phones
Once, you had to have co-invented GSM to make phones. Now, all components unbundled 2001, Nokia “Manufacturing is a core competency & competitive advantage” 75% of Nokia phones made in 8 Nokia factories 2016, Apple The iPhone has 189 suppliers with 789 locations None owned by Apple

70 Meanwhile, progress to autonomy
Steady, incremental progress, solving one tech challenge at a time Old-fashioned cruise control Smart cruise control & lane keeping Automatic, but driver needs to be ready to take over Driver only needed in exceptions, bad weather etc No controls, no driver, no cabin 1958 Now 5-10 years? Source: VDA, a16z 1 2 3 4 5 Level Level

71 Physical scale Electric changes
barriers to entry… But bashing metal is still about scale Data network effects Driving behaviour data to feed ML HD 3D maps How many winners? On-demand network effects Virtuous circle: density of cars + density of riders Uber + Lyft Where is the competitive moat in cars? Once batteries and sensors are commodities, where are the strategic levers? Still lots of unanswered questions

72 Self-driving car = horseless
carriage First, we make the tech fit the old patterns. Then, we change to take advantage of what’s new. And removing the driver changes even more than removing the horses. Source: Daimler

73 The obvious impact… It’s
easy to talk about the obvious impacts of electric and autonomy – oil and safety Half of oil production 1.25m annual road deaths

74 But the second-order effects
will be much bigger Electric changes the whole car ecosystem Half of oil production 1.25m annual road deaths Service & repair with 10x fewer moving parts (And what else gets sold in 150k US gas stations?) Power generation & storage Taxes? Machine tools